Balance Model Research Articles

A balanced mineral prospectivity model of Canadian magmatic Ni (± Cu ± Co ± PGE) sulphide mineral systems using conditional variational autoencoders

With the increasing demand for raw materials, innovative exploration techniques are needed to discover large mineral deposits that are accessible from the surface. In recent years, various supervised machine learning techniques have proven effective for mineral prospectivity modelling (MPM). However, the successful application of these techniques has been limited due to the scarcity of known mineral deposits compared to barren regions, which leads to a model imbalance favouring the latter. We address the data imbalance challenge in MPM by proposing a novel generative modelling approach using a conditional variational autoencoder (CVAE). We compare the proposed method with two other data balancing techniques, namely the synthetic minority oversampling technique and class weighting. Furthermore, the efficacy of the balancing strategies is evaluated for three MPM classification methods, including extreme gradient boosting machines (XGBM), random forests, and multilayer perceptrons. We implement and test the approaches by modelling the prospectivity of magmatic Ni (±Cu ±Co ±Platinum group elements) sulphide mineral systems for the Canadian landmass. With an area under the success rate curve of 0.95 for a spatially distinct testing data set, we observe that a combination of the proposed CVAE framework with the XGBM classification model surpasses the other methods. Furthermore, the geographical representation of our XGBM-CVAE model demonstrates a strong association with known Ni mineral occurrences in Canada, along with new prospective regions in underexplored areas.

Knowledge distillation with resampling for imbalanced data classification: Enhancing predictive performance and explainability stability

Machine learning classification models often struggle with imbalanced datasets, leading to poor performance in minority classes. While preprocessing approaches like resampling can improve minority class detection, they may introduce sampling bias and reduce model explainability. This study proposes a novel method combining random undersampling (RUS) with knowledge distillation (KD) to enhance both predictive performance and explainability stability for imbalanced data classification. Our approach employs a two-step learning process: (1) training a balanced teacher model using RUS and (2) training an imbalanced student model through response-based KD, utilizing both soft and hard targets. We hypothesize that this method mitigates class imbalance while preserving important information from the original dataset. We evaluated our proposed model against baseline and RUS-only models using five diverse imbalanced datasets from various domains. Performance was assessed using stratified 10-fold cross-validation with ROC-AUC and PR-AUC scores. Explainability stability was measured by the cosine similarity of SHAP values across cross-validation folds. Results demonstrate that our proposed model consistently outperforms both baseline and RUS-only models regarding ROC-AUC and PR-AUC scores across all datasets. Moreover, it exhibits superior explainability stability in the majority of cases, addressing the sampling bias issue associated with traditional resampling methods. This research contributes to the field of machine learning by offering a novel approach that simultaneously improves predictive performance and maintains explainability for imbalanced data classification.

Analyzing a New Portfolio Balance Model with Micro and Macro Determinants of Exchange Rate when Expectations are Rational

Analyzing a New Portfolio Balance Model with Micro and Macro Determinants of Exchange Rate when Expectations are Rational

Combined optimization strategy: CUBW for load balancing in software defined network

Software Defined Network (SDN) facilitates a centralized control management of devices in network, which solves many issues in the old network. However, as the modern era generates a vast amount of data, the controller in an SDN could become overloaded. Numerous investigators have offered their opinions on how to address the issue of controller overloading in order to resolve it. Mostly the traditional models consider two or three parameters to evenly distribute the load in SDN, which is not sufficient for precise load balancing strategy. Hence, an effective load balancing model is in need that considers different parameters. Considering this aspect, this paper presents a new load balancing model in SDN is introduced by following three major phases: (a) work load prediction, (b) optimal load balancing, and (c) switch migration. Initially, work load prediction is done via improved Deep Maxout Network. COA and BWO are conceptually combined in the proposed hybrid optimization technique known as Coati Updated Black Widow (CUBW). Then, the optimal load balancing is done via hybrid optimization named Coati Updated Black Widow (CUBW) Optimization Algorithm. The optimal load balancing is done by considering migration time, migration cost, distance and load balancing parameters like server load, response time and turnaround time. Finally, switch migration is carried out by considering the constraints like migration time, migration cost, and distance. The migration time of the proposed method achieves lower value, which is 27.3%, 40.8%, 24.40%, 41.8%, 42.8%, 42.2%, 40.0%, and 41.6% higher than the previous models like BMO, BES, AOA, TDO, CSO, GLSOM, HDD-PLB, BWO and COA respectively. Finally, the performance of proposed work is validated over the conventional methods in terms of different analysis.

The thrust balance model during the dragonfly hovering flight

In recent years, the micro air vehicle (MAV) oscillations caused by thrust imbalances have received more attention. This paper proposes a dual-wing thrust balance model (DTBM) that can solve the above problem by iterating the modified rotation angle formula. The core control parameter of the DTBM model is the au angle, which refers to the angle between the wing surface and the stroke plane at the mid-stroke position during the upstroke. For each degree change in the au angle, the range of variation in the dimensionless average thrust coefficient is between 0.0225-0.0268. A thrust coefficient of 0.0225 causes the dragonfly to move forward by 9.037 cm in one second, which is equivalent to 1.29 times its body length. By using DTBM, the average thrust coefficient can be reduced to below 0.001 in just a few iterations. No matter how complex the motion pattern is, the DTBM can achieve thrust balance within 0.278 s. Through our research, when selecting the deviation angle motion of real dragonflies, the dual-wing au angles exhibit a highly linear correlation with wing spacing, called linear motion. In contrast, the nonlinear variation of the au angle appears in the hindwing of the no-deviation motion and the forewing of the elliptical deviation motion. All of the nonlinear changes are referred to as nonlinear motion. Nonlinear variation of the au angle arises from larger disturbances of the lateral force during the upstroke. The stronger lateral force is closely related to the flapping trajectory. When the flapping trajectory causes the dual-wing to closely approach each other in the mid-stroke, a continuous positive pressure zone forms between the dual-wing. The collision of the leading-edge vortex and the shedding of the trailing-edge vortex is the special flow field structure in the nonlinear motion. Guided by the DTBM, future designs of MAVs will be able to better achieve thrust balance during hovering flight, requiring only the embedding of the iteration algorithm and prediction function of the DTBM in the internal chip.

Balanced d-Band Model: A Framework for Balancing Redox Reactions in Lithium-Sulfur Batteries.

Managing the redox reactions of polysulfides is crucial for improving the performance of lithium-sulfur batteries (LSBs). Herein, we introduce a progressive theoretical framework: the balanced d-band model, which is based on classical d-band center theory. Specifically, by optimizing the position of the d-band center in the middle between the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) of each sulfur species, balanced and fast oxidation and reduction reactions of the sulfur species can be achieved simultaneously. To validate this theory, we synthesized a catalyst featuring an in situ phosphorized heterostructure (NOP) based on nickel oxide (NiO), which effectively optimizes the d-band center at the middle between the HOMO and LUMO of each sulfur species. Aided by the balanced oxidation and reduction kinetics of the sulfur species, the NOP-based cell achieved a high reversible capacity, superior cycling stability, and prolonged cycle life. This study extends the conventional d-band center theory and introduces an innovative theoretical model to expand our understanding of the internal reaction mechanisms in LSBs.

Risk Management Strategies and Performance of Selected Insurance Companies in Nairobi City County, Kenya

The insurance industry plays a vital role in the management of risk and therefore helps to support and facilitate business activities. The performance of insurance companies is impacted by various challenges related to their risk management strategies. These challenges can arise from both internal and external factors, and they can have significant implications for the financial stability and profitability of insurance companies. Changes in regulatory requirements can impact the risk management strategies of insurance companies, requiring them to adapt their practices and systems to remain compliant. Therefore, this study seeks to investigate the influence of risk management strategies on the performance of selected insurance companies in Nairobi City County, Kenya. The study was guided by the balanced scorecard model, agency theory, portfolio theory, risk aversion theory, and prospect theory. A descriptive research design was employed. The study targeted 95 respondents, comprising 10 senior-level managers, 30 middle-level managers, and 55 departmental heads from the insurance companies operating in Kenya. A census of 95 respondents was conducted. The study used a structured questionnaire. A pilot group of 9 individuals was selected from the target population. Content validity was applied to the study. Cronbach’s Alpha coefficient was computed for all components of the questionnaire, and their assessment was provided. Descriptive analysis was computed using mean, frequencies, and percentages. Regression analysis tested the relationship between the study variables. The regression model assessed the relationship between the independent variables and the dependent variable. The findings were presented using tables and figures. Through regression analysis with a linear model result showed a strong relationship between risk management strategies and performance. The resulting value of regression coefficient at .000 (p<0.05) indicated existence of relationships that were relatively strong. The study thus concluded that performance of insurance companies is dependent on the careful choice or selection of risk management strategies. It is expected the results will benefit various stakeholders, including the government policy makers, Insurance sector players, and academia. Further studies exploring more strategies to manage risks in other entities including other sectors such as the finance sector could be undertaken by scholars.

Hydrochemistry and surface water - groundwater interactions in an anthropically disturbed mountain river (Sierras Pampeanas, central Argentina)

The surface water - groundwater interactions in a small anthropically affected mountain watershed (Salsipuedes River) in the Sierras Pampeanas (central Argentina) were analyzed and quantified by means of 222Rn mass balance modeling, and its implications in hydrochemistry and water quality were examined. Surface waters, springs and groundwater were sampled during the 2021–2022 hydrological year, and the major dissolved composition was determined. The studied waters are alkaline, showing an evolution from medium mineralized (1.5 < ∑+ < 3 meq L−1) to mineralized type (3 < ∑+ < 6 meq L−1) downstream. In general, waters are of the bicarbonate - calcium type with no spatial or seasonal variations. This composition is mainly controlled by silicates weathering. 222Rn modeling indicates that near the headwaters the Salsipuedes River recharges the aquifer, losing 1.2% and 2.4% of its discharge during the dry and wet seasons, respectively. Downstream, the system changes its hydrological behavior and the river acts as a gaining stream; i.e, it receives groundwater inflow. The magnitude of this inflow varies seasonally, being greater in the wet season (1.5–2.2% of the total flow) than in the dry season (less than 0.25%) due to a rise in the water table level as a result of direct recharge of the phreatic aquifer. Anthropic influence is noticeable in nitrate and chloride concentrations, which are derived from domestic wastes and septic tanks. The highest concentrations of these ions in surface waters are found in the urbanized area, where the river stretch corresponds to a gaining stream, reflecting that even small amounts of groundwater inputs can alter the chemical composition of streams. A temporal analysis also showed a variable response of the system to the increasing anthropic pressure and climatic events such as ENSO, which affect not only water availability but also its quality, highlighting the vulnerability of small mountain watersheds.

Evaluating potential power output of terrestrial thermoradiative diodes with atmospheric modeling

A thermoradiative diode is a device that can generate power through thermal emission from the warm Earth to the cold night sky. Accurate assessment of the potential power output requires knowledge of the downwelling radiation from the atmosphere. Here, accurate modeling of this radiation is used alongside a detailed balance model of a diode at the Earth's surface temperature to evaluate its performance under nine different atmospheric conditions. In the radiative limit, these conditions yield power densities between 0.34 and 6.5W.m-2, with optimal bandgaps near 0.094 eV. Restricting the angles of emission and absorption to less than a full hemisphere can marginally increase the power output. Accounting for non-radiative processes, we suggest that if a 0.094 eV device would have radiative efficiencies more than two orders of magnitude lower than a diode with a bandgap near 0.25 eV, the higher bandgap material is preferred.

Evaluation of Air Traffic Network Resilience: A UK Case Study

With growing air travel demand, weather disruptions cost millions in flight delays and cancellations. Current resilience analysis research has been focused on airports and airlines, rather than the en-route waypoints, and has failed to consider the impact of disruption scenarios. This paper analyses the resilience of the United Kingdom (UK) air traffic network to weather events that disrupt the network’s high-traffic areas. A Demand and Capacity Balancing (DCB) model is used to simulate adverse weather and re-optimise the cancellation, delay, and rerouting of flights. The model’s feasibility and effectiveness were evaluated under 20 concentrated and randomly occurring extreme disruption scenarios, lasting 2 h and 4 h. The results show that the network is vulnerable to extended weather events that target the network’s most central waypoints. However, the network demonstrates resilience to weather disruptions lasting up to two hours, maintaining operational status without any flight cancellations. As the scale of disruption increases, the network’s resilience decreases. Notably, there exists a threshold beyond which further escalation in disruption scale does not significantly impair the network’s performance.

The source and differential enrichment mechanisms of lithium in Gudui geothermal field: Constraints from enrichment and dilution processes of geothermal-type lithium

As a typical high-temperature geothermal system in the Qinghai-Xizang Plateau, the Gudui geothermal field (GDGF) shows notably high lithium content. Compared with other Li-rich geothermal fields, GDGF exhibits differential enrichment of lithium. Furthermore, few studies have focussed on investigation of the depletion of lithium during the ascent of geothermal fluids. Therefore, this study investigated the source, mechanism of differential enrichment of lithium and depletion mechanism of lithium. The δD and δ18O plot and the two-endmember modeling of Sr isotope indicate that geothermal fluids in GDGF were affected by magmatic water and mixing ratios are range from 0.8 to 12 %. Furthermore, the Li isotope mass balance model shows that the leucogranite distributed near GDGF is the primary source of lithium in geothermal fluids. The correlations between Na, K, Li and Cl concentrations indicate that the geothermal waters in eastern and western region originate from different parent geothermal fluids. The δD-Cl plot and enthalpy-chloride diagram reveals that the geothermal water in WGDGF was mainly formed by mixing with shallow surface water, and multiple boiling processes took place in EGDGF. In addition, Lithium in the geothermal fluids might be absorbed by the clay minerals widely distributed in the EGDGF, which causes the depletion in the rise process. In conclusion, the differential enrichment model of lithium in GDGF was constructed.

Influence of species kinetics on discharge characteristics in oxygen helicon plasma

Abstract Oxygen (O2) helicon plasmas in multiple wave modes were excited by a right-helical antenna with an upper metal endplate at low pressure. Mode transitions were observed at increasing input power or magnetic field, characterized by obvious jumps of plasma parameters. Blue Core appears at high magnetic fields (~700 G) and input powers (~1700 W), with a large radial gradient of plasma density, ion line intensity, and electron temperature. Emission spectra demonstrate that the blue lights originate from O II lines. We found that the intensity ratio of O II to O I of Blue Core in O2 is lower by one order than that in N2 or Ar despite their similar ionization rates and plasma densities in Blue Core area. A high-temperature B-dot probe together with a waveform fitting procedure was used to present the measured oscillating waveforms of m =+1 helicon waves, showing distinct wave structures of different eigenmodes. Cavity mode resonance is suggested to be responsible for the formation of standing waves of discrete eigenmodes. A pressure balance model was developed to estimate the species densities around the central area in different modes, showing massive dissociation of O2 molecules and high density of O atoms locally, so that O2 helicon plasma behaves a species feature of monatomic gas discharge. The obviously low intensity of O II lines compared to O I lines of Blue Core in O2 is related to the quite high excitation threshold of O+ ions (~30 eV) although electron density and temperature are relatively high. The combined effects of dispersed reaction energy distribution, massive molecule dissociation and negative ion creation are considered to be the main causes for requirement of much higher RF power and magnetic field for Blue Core formation in O2 helicon plasma than that in Ar. The calculated radial profile of power deposition and the captured plasma morphology confirm that the dominant central electron heating is the essential reason for the large radial gradients of plasma density and electron temperature which contributes to the serious neutral depletion and Blue Core formation.

Features of Providing Air Temperature and Humidity Parameters in the Engine Room Working Area at Sewage Pumping Stations

Statement of the problem. There is an important task for engineers to improve the working conditions of workers in industrial facilities. The waste water system includes a large number of structures with different temperature and humidity microclimate characteristics. The possibility has been discussed to improve these characteristics by redistributing air flows in the engine room of a city sewage pumping station with PLI panel. Results. There are the results of the temperature and humidity microclimate parameters research in the working area of the engine room at a high-voltage sewage pumping station which is located in Moscow. The author has proved the possibility of use the cooled air from the machine room lower level in a high-voltage pumping station at the machine room main working area. It has been described the calculation features of the heat transfer coefficients in the proposed model for local ventilation at the main technological working area. It has been described the results of temperature and humidity parameters instrumental study in the main working area and the lower level. Conclusion. Thelocal ventilation systems could be used for the working area, where an employee performs repair work of the main technical equipment in a sewage pumping station. The balanced mathematical thermal model includes PLI panel’s potential location.

Macronutrient interactions and models of obesity: Insights from nutritional geometry.

The global obesity epidemic results from a complex interplay of genetic and environmental factors, with diet being a prominent modifiable element driving weight gain and adiposity. Although excess intake of energetic macronutrients is implicated in causing obesity, ongoing debate centers on whether sugar or fat or both are driving the rising obesity rates. This has led to competing models of obesity such as the "Carbohydrate Insulin Model", the "Energy Balance Model", and the "Fructose Survival Hypothesis". Conflicting evidence from studies designed to focus on individual energetic macronutrients or energy rather than macronutrient mixtures underlies this disagreement. Recent research in humans and animals employing the nutritional geometry framework (NGF) emphasizes the importance of considering interactions among dietary components. Protein interacts with carbohydrates, fats, and dietary energy density to influence both calorie intake ("protein leverage") and, directly and indirectly, metabolic physiology and adiposity. Consideration of these interactions can help to reconcile different models of obesity, and potentially cast new light on obesity interventions.

Remote Sensing Data Assimilation to Improve the Seasonal Snow Cover Simulations Over the Heihe River Basin, Northwest China

ABSTRACTThe reliability of seasonal snow cover information is constrained by limitation of in situ observations and uncertainties in remote sensing data and model simulations in alpine region, thus posing important challenges to understanding the climate system and water resource management in alpine region. Here, the assimilation of daily cloud‐free Moderate Resolution Imaging Spectroradiometer (MODIS) normalised difference snow index (NDSI) product into an intermediate complexity snow mass and energy balance model—Flexible Snow Model (version FSM2_MO)—was implemented. The aim is to improve the model simulations of seasonal snow cover (snow‐covered extent; SCE, snow depth; SD, snow water equivalent; SWE, and snowmelt runoff; SMR) in the alpine region (a case of the upper‐middle reaches of the Heihe River basin, Northwest China). The results indicate comprehensive improvement in the simulation of SCE, SD, and SMR in the study area through data assimilation, with the ability to significantly reduce prior biases of the FSM2_MO. Based on the independent daily cloud‐free Advanced Very High Resolution Radiometer (AVHRR) SCE product, the updated SCE simulation (i.e., data assimilation) showed a reduction in mean absolute error (MAE) from 10.46% to 7.16%, root mean square error (RMSE) from 16.14% to 12.26%, and an increase in Pearson's correlation coefficient (CC) from 0.18 to 0.67 compared with the open loop simulation (i.e., without assimilation). The evaluation results of SD observation data showed that data assimilation improved SD simulation compared with the open loop run (OL). And utilising the monthly discharge observations at the Yingluoxia hydrological station, data assimilation slightly improved the SMR simulation. The updated SMR simulation achieved a CC of 0.91, Nash‐Sutcliffe efficiency coefficient (NSE) of 0.73, and Kling‐Gupta efficiency coefficient (KGE) of 0.76. Moreover, the Landsat 8‐derived snow cover map and Sentinel‐1‐derived SD also indicated that the updated simulation effectively filled in the missing snow cover and removed the superfluous snow cover predicted by the OL simulation in terms of spatial distribution.

Application of Hydrochemical Parameters as Tool for Sustainable Management of Water Supply Network

Effective management of Water Supply Systems (WSSs) is crucial for ensuring safe drinking water. The WSS of Varaždin County is a complex network involving three groundwater sources: Bartolovec and Vinokovščak wellfields (alluvial aquifers) and Bela karstic spring. To achieve a comprehensive characterization of WSSs, routine laboratory data was integrated with stable isotopes and geochemical modeling. Within this study, all measured parameters remain below the Maximum Contaminant Level (MCL), ensuring water safety for human consumption. The Piper diagram identified variations in water sources based on their chemical composition, providing a simplified overview of mixing patterns within WSSs. Among the modeling approaches, inverse modeling (IM) was found to be more reliable than forward modeling (FM) and mass balance modeling (MB). Despite the limited capacity of δ18O to provide accurate mixing results, it was revealed that the reservoir water was in equilibrium with the air (no evaporation effects), indicating well-sealed reservoirs. Mixing modeling showed that the western, southwestern, and northern parts of the WSS mixed all three sources, whereas the eastern and southeastern areas primarily relied on the deeper aquifer of the Bartolovec source, indicating potential vulnerability. Strict validation criteria ensured the reliability of results, demonstrating the effectiveness and applicability of geochemical modeling in water security management plans.

A Novel Machine Learning-Based Predictive Model of Clinically Significant Prostate Cancer and Online Risk Calculator

ObjectivesTo create a machine learning predictive model combining PI-RADS score, PSA density, and clinical variables to predict clinically significant prostate cancer (csPCa). MethodsWe evaluated a cohort of patients who underwent prostate biopsy for suspected prostate cancer (PCa) in New Zealand, Australia, and Switzerland. We collected data on age, body mass index (BMI), PSA level, prostate volume, PSA density (PSAD), PI-RADS scores, previous biopsy, and corresponding histology results. The dataset was divided into derivation (training) and validation (test) sets using random splits. An independent dataset was obtained from the Harvard Dataverse for external validation. A cohort of 1272 patients was analyzed. We fitted a Lasso model, XGBoost, and LightGBM to the training set and assessed their accuracy. ResultsAll models demonstrated ROC AUC values ranging from 0.830 to 0.851. LightGBM was considered the superior model, with an ROC of 0.851 [95%CI: 0.804 – 0.897] in the test set and 0.818 [95% CI: 0.798 – 0.831] in the external dataset. The most important variable was PI-RADS, followed by PSA density, history of previous biopsy, age, and BMI. ConclusionsWe developed a predictive model for detecting csPCa that exhibited a high ROC-AUC value for internal and external validations. This suggests that the integration of the clinical parameters outperformed each individual predictor. Additionally, the model demonstrated good calibration metrics, indicative of a more balanced model than the existing models.

GUIDELINES TO CREATE WORK-LIFE BALANCE IN INDUSTRIAL BUSINESS ORGANIZATIONS IN THE DIGITAL ERA

Objective: Approaches to achieving work-life balance in industrial organizations during the digital era are investigated, with the aim of developing a structural equation model to enhance managerial effectiveness and competitiveness. Theoretical Framework: Survey research and mixed-methods statistical frameworks are employed, integrating qualitative and quantitative data to explore work-life balance in industrial organizations. Descriptive, inferential, and multivariate statistics are used to assess relationships and evaluate structural equation model's fit with the data. Method: Qualitative research is conducted through in-depth interviews with 9 experts and focus group discussions with 11 specialists. Quantitative research is conducted via a survey of 500 industrial business executives. Data are analyzed using descriptive, inferential, and multivariate statistics. Results and Discussion: Four key components to achieving work-life balance are identified: 1) Discipline and Motivation (X̅ = 4.32), 2) Innovative Activities (X̅ = 4.31), 3) Work Environment (X̅ = 4.29), 4) Organizational Support (X̅ = 4.22). A significant difference (p &lt; 0.05) in approaches is revealed between small-to-medium sized businesses and large organizations. Statistical analyses indicated the structural equation model met the evaluation criteria and aligned well with observational data. Research Implications: This research provides practical guidance for implementing effective work-life balance strategies in industrial organizations while advancing theoretical understanding of key factors influencing employee morale, productivity, and competitiveness. Originality/Value: This study contributes to the literature by developing a structural equation model for work-life balance in digital-era industrial organizations, offering new insights into compensation, innovation, and workplace dynamics, with the potential to reshape organizational practices and enhance managerial effectiveness.

Evaluating the effects of climate change on managed lowland rivers in the California Bay Delta Watershed

Water that is released from reservoirs can affect the downstream thermal regimes of rivers. During the summer months, these flow releases can lower the river temperature downstream of dams in an extension that mainly depends on the volume and temperature of these releases and the energy exchange with the overlying atmosphere. The benefit of this cooling effect has been suggested as an approach to mitigate the effects of climate change in downstream-regulated rivers. However, anticipated climate change conditions may weaken these cooling benefits, especially in managed lowland rivers (MLRs), as they are subjected to large withdrawals, are shallow, and convey clear water. Here, we show that MLRs in the California Bay Delta Watershed are vulnerable to water temperature increases, especially during future summer months subjected to a future high-emission greenhouse scenario. Low-flow conditions exacerbate this vulnerability, especially at locations downstream of high-flow diversions. By using a physical energy balance model (FLUVIAL-EB) paired with a downscaled climate regional model (CRCM5-RCP8.5), we found that for summer months between 2030 and 2100, longwave and latent heat fluxes will contribute to water temperature increases, while absorbed solar radiation will likely decrease under future climate scenarios. Despite the warming effects of climate change on MLRs in the California Bay Delta Watershed, our findings suggest that increasing the release of hypolimnetic water from reservoirs during summer months can be a viable solution to mitigate the river temperature increase.

Long-term variations of factors controlling REE-rich mud formation based on ocean–sediment Nd mass balance modeling

Long-term variations of factors controlling REE-rich mud formation based on ocean–sediment Nd mass balance modeling